Geographically weighted regression analysis for nonnegative continuous outcomes: An application to Taiwan dengue data.
Geographically Weighted Regression (GWR) has gained widespread popularity across various disciplines for investigating spatial heterogeneity with respect to data relationships in georeferenced datasets. However, GWR is typically limited to the analysis of continuous dependent variables, which are as...
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| Format: | Article |
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Public Library of Science (PLoS)
2024-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0315327 |
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| author | Vivian Yi-Ju Chen Yun-Ciao Yang |
| author_facet | Vivian Yi-Ju Chen Yun-Ciao Yang |
| author_sort | Vivian Yi-Ju Chen |
| collection | DOAJ |
| description | Geographically Weighted Regression (GWR) has gained widespread popularity across various disciplines for investigating spatial heterogeneity with respect to data relationships in georeferenced datasets. However, GWR is typically limited to the analysis of continuous dependent variables, which are assumed to follow a symmetric normal distribution. In many fields, nonnegative continuous data are often observed and may contain substantial amounts of zeros followed by a right-skewed distribution of positive values. When dealing with such type of outcomes, GWR may not provide adequate insights into spatially varying regression relationships. This study intends to extend the GWR based on a compound Poisson distribution. Such an extension not only allows for exploration of relationship heterogeneity but also accommodates nonnegative continuous response variables. We provide a detailed specification of the proposed model and discuss related modeling issues. Through simulation experiments, we assess the performance of this novel approach. Finally, we present an empirical case study using a dataset on dengue fever in Tainan, Taiwan, to demonstrate the practical applicability and utility of our proposed methodology. |
| format | Article |
| id | doaj-art-e272e6581f6143d9b48ebc00da22394c |
| institution | Kabale University |
| issn | 1932-6203 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj-art-e272e6581f6143d9b48ebc00da22394c2024-12-17T05:31:49ZengPublic Library of Science (PLoS)PLoS ONE1932-62032024-01-011912e031532710.1371/journal.pone.0315327Geographically weighted regression analysis for nonnegative continuous outcomes: An application to Taiwan dengue data.Vivian Yi-Ju ChenYun-Ciao YangGeographically Weighted Regression (GWR) has gained widespread popularity across various disciplines for investigating spatial heterogeneity with respect to data relationships in georeferenced datasets. However, GWR is typically limited to the analysis of continuous dependent variables, which are assumed to follow a symmetric normal distribution. In many fields, nonnegative continuous data are often observed and may contain substantial amounts of zeros followed by a right-skewed distribution of positive values. When dealing with such type of outcomes, GWR may not provide adequate insights into spatially varying regression relationships. This study intends to extend the GWR based on a compound Poisson distribution. Such an extension not only allows for exploration of relationship heterogeneity but also accommodates nonnegative continuous response variables. We provide a detailed specification of the proposed model and discuss related modeling issues. Through simulation experiments, we assess the performance of this novel approach. Finally, we present an empirical case study using a dataset on dengue fever in Tainan, Taiwan, to demonstrate the practical applicability and utility of our proposed methodology.https://doi.org/10.1371/journal.pone.0315327 |
| spellingShingle | Vivian Yi-Ju Chen Yun-Ciao Yang Geographically weighted regression analysis for nonnegative continuous outcomes: An application to Taiwan dengue data. PLoS ONE |
| title | Geographically weighted regression analysis for nonnegative continuous outcomes: An application to Taiwan dengue data. |
| title_full | Geographically weighted regression analysis for nonnegative continuous outcomes: An application to Taiwan dengue data. |
| title_fullStr | Geographically weighted regression analysis for nonnegative continuous outcomes: An application to Taiwan dengue data. |
| title_full_unstemmed | Geographically weighted regression analysis for nonnegative continuous outcomes: An application to Taiwan dengue data. |
| title_short | Geographically weighted regression analysis for nonnegative continuous outcomes: An application to Taiwan dengue data. |
| title_sort | geographically weighted regression analysis for nonnegative continuous outcomes an application to taiwan dengue data |
| url | https://doi.org/10.1371/journal.pone.0315327 |
| work_keys_str_mv | AT vivianyijuchen geographicallyweightedregressionanalysisfornonnegativecontinuousoutcomesanapplicationtotaiwandenguedata AT yunciaoyang geographicallyweightedregressionanalysisfornonnegativecontinuousoutcomesanapplicationtotaiwandenguedata |